Computer Analysis Of Low-complexity Amino Acid And Nucleotide Sequences

低复杂性氨基酸和核苷酸序列的计算机分析

• 批准号: 7969201
• 负责人: JOHN C. WOOTTON
• 金额: $ 26.72万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969201
• 关键词: 3-Dimensional; Accounting; Affect; Algorithms; Amino Acid Repetitive Sequences; Amino Acid Sequence; Amino Acids; Base Sequence; Chemicals; Codon Nucleotides; Communicable Diseases; Computer Analysis; Computing Methodologies; Crystallography; DNA; Databases; Development; Embryonic Development; Evolution; Foundations; Frequencies; Genome; Genomics; Goals; Immune; Investigation; Knowledge; Malaria; Malaria Vaccines; Methods; Molecular Conformation; Molecular Structure; Mutation; Nucleotides; Organism; Parasites; Pathology; Pattern; Peptide Sequence Determination; Property; Protein Region; Proteins; RNA Processing; Research; Role; Sequence Analysis; Signal Transduction; Structure; Transcriptional Regulation; Variant; base; extracellular; genome-wide; improved; microorganism; molecular mechanics; pressure; quantum; research study; theories; vaccine development

We are investigating segments of protein and nucleotide sequences that show compositional bias and raise several challenges for computational analysis. We develop methods to help to understand the structural, functional and evolutionary significance of these regions and their pathology. The sequences include local low complexity regions or domains, including conformationally mobile or intrinsically unstructured regions of proteins, and tandemly-repeated sequences. Further problems arise from more generally distributed amino acid content biases that can reflect directional mutation pressures at the genomic level and constraints specific to protein or domain function. Low complexity regions comprise a large proportion of the genome-encoded amino acids, and may contain homopolymeric tracts or mosaics of a few amino acids, or repeated patterns, frequently subtle, including those typical of many non-globular domains and dynamic or intrinsically unstructured segments of proteins. We have developed mathematical definitions and algorithms to define and identify regions of compositional bias, and to discover and analyze properties of these regions relevant to their structures, interactions, and evolution. Local regions of low complexity and tandemly repeated amino acid sequences occur in many proteins involved in cellular differentiation and embryonic development, RNA processing, transcriptional regulation, signal transduction and aspects of cellular and extracellular structural integrity. Segments of proteins are commonly non-globular, intrinsically unstructured, or conformationally mobile: however, knowledge of the molecular structures and dynamics of these domains is still very limited. They are generally relatively intractable to investigation by crystallography and NMR, and they still account for less than 1% of the residues in 3-dimensional structural databases. Current computer methods based on molecular mechanics and dynamics have given inconsistent results when applied to low-complexity amino acid sequences. Accordingly, we are experimenting with ab initio quantum chemical methods to investigate the ensembles of conformational states accessible to these regions of proteins. As specific examples to motivate this development, we are investigating amino acid sequence repeats of malaria parasites with possible roles in immune evasion as components of malaria vaccines. A related problem is compositional bias that can affect not only local segments but is distributed generally over the entire genome or proteins of an organism. This is shown, for example, by the biases in codons or proteins encoded by very AT-rich or GC-rich genomes including those of several important infectious disease organisms. Such variation and bias in genome-wide amino acid and nucleotide compositions raise problems for several commonly used sequence analysis algorithms. Accordingly, current research with Stephen Altschul and Yi-Kuo Yu is developing the theoretical foundation and implementation of these algorithms further in ways that include an improved treatment of background frequencies.

我们正在研究蛋白质和核苷酸序列的片段，这些序列显示出组成偏差并为计算分析带来了一些挑战。 我们开发了帮助了解这些区域及其病理的结构，功能和进化意义的方法。 这些序列包括局部低复杂性区域或域，包括蛋白质的构象移动或本质上的非结构化区域以及串联重复的序列。 进一步的问题是由更普遍的分布的氨基酸含量偏见引起的，这些氨基酸含量偏见可以反映基因组水平上的方向突变压力以及特定于蛋白质或结构域功能的约束。 低复杂性区域包括很大一部分基因组编码的氨基酸，并且可能包含几种氨基酸或重复模式的均聚糖区或镶嵌物，通常是微妙的，包括许多非全球域的典型型号，以及蛋白质的非整体域或固有非结构性片段。我们已经开发了数学定义和算法来定义和确定组成偏差的区域，并发现和分析与它们的结构，相互作用和进化相关的这些区域的属性。低复杂性和串联重复的氨基酸序列的局部区域发生在许多参与细胞分化和胚胎发育，RNA处理，转录调控，信号转导以及细胞外结构完整性方面的蛋白质中。蛋白质的片段通常是非整体，本质上非结构化或构象移动的：但是，对这些域的分子结构和动力学的了解仍然非常有限。它们通常与晶体学和NMR的研究相对棘手，并且在3维结构数据库中仍然不到1％的残基。 当应用于低复杂性氨基酸序列时，基于分子力学和动力学的当前计算机方法给出了不一致的结果。 因此，我们正在尝试量子化学方法，以研究这些蛋白质区域可访问的构象状态的集合。 作为激励这种发展的具体例子，我们正在研究疟疾寄生虫的氨基酸序列重复序列，在免疫逃避中可能作用为疟疾疫苗的成分。 一个相关的问题是组成偏差，不仅可以影响局部细分，而且通常在生物体的整个基因组或蛋白质上分布。 例如，由非常丰富或富含GC的基因组（包括几种重要的感染性疾病生物的基因组）所编码的密码子或蛋白质的偏见显示了这一点。 全基因组氨基酸和核苷酸组成的这种变异和偏差引起了几种常用序列分析算法的问题。 因此，目前对Stephen Altschul和Yi-Kuo Yu的研究正在以包括改进的背景频率处理的方式进一步发展这些算法的理论基础和实施。